### REPLICATION FILE -- APPENDIX, Germany
### Jonathan Homola & Margit Tavits
### "Contact Reduces Immigration-Related Fears for Leftist
### but Not for Rightist Voters"
### Comparative Political Studies

## clear environment, set seed, install/load packages
rm(list=ls())
set.seed(12435); options(stringsAsFactors=F)
# install.packages("stargazer"); install.packages("psych")
# install.packages("xtable")
library("stargazer"); library("psych"); library("xtable")

## set working directory and source interaction functions
setwd(" ... ")
source("HomolaTavits_ContactFears_interactions.R")

## read in the main dataset
data <- read.csv("HomolaTavits_ContactFears_Germany.csv")

## recode "not finished school" from 5 to 1
data$education[data$education==5] <- 1

# replace missing data codes with NA
is.na(data) <- data==666
is.na(data) <- data==777
is.na(data) <- data==977

## create contact factor score
contact <- prcomp(~ as.numeric(contact1) + as.numeric(contact2) + as.numeric(contact3), 
                  data=data, na.action = na.exclude)
data$contact <- contact$x[,1]

## Eigenvalue of first and second factor
(contact$sdev^2*3/sum(contact$sdev^2))[1]  ## 1.62
(contact$sdev^2*3/sum(contact$sdev^2))[2]  ## 0.75

## create threat factor score
threat <- prcomp(~ as.numeric(violence_com) + as.numeric(violence_nat) + 
                   as.numeric(econ_hh) + as.numeric(econ_nat) +
                   as.numeric(nat_id) + as.numeric(ger_cult),
                 data=data, na.action = na.exclude)
data$threat <- -threat$x[,1]

## Eigenvalue of first and second factor
(threat$sdev^2*6/sum(threat$sdev^2))[1]  ## 4.28
(threat$sdev^2*6/sum(threat$sdev^2))[2]  ## 0.54

## Left/Right coding
## right vote2013 (1=CDU, 5=FDP, 6=AFD)
## left vote2013 (2=SPD, 3=Linke, 4=Greens)
## NA (7=Others, 8=no vote, 9=NA)
data$right <- NA
data$right[data$vote2013==1] <- 1
data$right[data$vote2013==5] <- 1
data$right[data$vote2013==6] <- 1
data$right[data$vote2013==2] <- 0
data$right[data$vote2013==3] <- 0
data$right[data$vote2013==4] <- 0
data$left <- NA
data$left[data$vote2013==2] <- 1
data$left[data$vote2013==3] <- 1
data$left[data$vote2013==4] <- 1
data$left[data$vote2013==1] <- 0
data$left[data$vote2013==5] <- 0
data$left[data$vote2013==6] <- 0

## Left/Right coding including non-voters
## right virtual_BTW_13_Quote (1=CDU, 5=FDP, 6=AFD)
## left virtual_BTW_13_Quote (2=SPD, 3=Linke, 4=Greens)
## NA (7=Others, 8=no vote, 9=NA)
data$voters <- NA
data$voters[data$vote2013==1] <- 1
data$voters[data$vote2013==5] <- 1
data$voters[data$vote2013==6] <- 1
data$voters[data$vote2013==2] <- 0
data$voters[data$vote2013==3] <- 0
data$voters[data$vote2013==4] <- 0
data$voters[data$vote2013==8] <- 2
data$voters <- as.factor(data$voters)

## continuous Left/Right (Manifesto scores) coding
## 1 - CDU 5.13
## 2 - SPD 3.82
## 3 - Left 3.27
## 4 - Greens 4.02
## 5 - FDP 5.70
## 6 - AFD 4.86
## NA (7=Others, 8=no vote, 9=NA)
data$lrscale <- NA
data$lrscale[data$vote2013==1] <- 5.13
data$lrscale[data$vote2013==2] <- 3.82
data$lrscale[data$vote2013==3] <- 3.27
data$lrscale[data$vote2013==4] <- 4.02
data$lrscale[data$vote2013==5] <- 5.70
data$lrscale[data$vote2013==6] <- 4.86

### CHAPEL HILL DATA
## Chapel Hill: Position on Immigration Policy 
## (0 opposed to restrictions -- 10 favor restrictions)
## 1 - CDU 5.727273
## 2 - SPD 3.909091
## 3 - Left 4.0
## 4 - Greens 2.090909
## 5 - FDP 3.6
## 6 - AFD 9.3
## NA (7=Others, 8=no vote, 9=NA)
data$chimmig<- NA
data$chimmig[data$vote2013==1] <- 5.727273
data$chimmig[data$vote2013==2] <- 3.909091
data$chimmig[data$vote2013==3] <- 4.0
data$chimmig[data$vote2013==4] <- 2.090909
data$chimmig[data$vote2013==5] <- 3.6
data$chimmig[data$vote2013==6] <- 9.3




#### Table OA7.1: Descriptive Statistics
data$female <- as.numeric(data$female)-1
data$East <- NA
data$East[!is.na(data$east)] <- 0
data$East[data$east==2] <- 1
xtable(describe(data[,c("violence_com", "violence_nat",
                        "econ_hh", "econ_nat",
                        "nat_id", "ger_cult", "contact", "threat",
                        "right", "left", "lrscale",
                        "female", "age", "education", "income", 
                        "east")])[,c(2,3,4,8,9)])




#### Table OA7.2: Threat Factor Score
xtable(cor(cbind(data$violence_com, data$violence_nat,
                 data$econ_hh, data$econ_nat, data$nat_id,
                 data$ger_cult, data$threat), use="complete.obs"))





#### Table OA7.3: Contact Factor Score
xtable(cor(cbind(data$contact1, data$contact2, data$contact3,
                 data$contact), use="complete.obs"))




#### Table OA8.1: The Effect of Contact on Immigration-Related Threat, Germany
mod0 <- lm(threat ~ contact + as.factor(female) + age + education + income + as.factor(east), data)
mod1 <- lm(violence_com ~ contact + as.factor(female) + age + education + income + as.factor(east), data)
mod2 <- lm(violence_nat ~ contact + as.factor(female) + age + education + income + as.factor(east), data)
mod3 <- lm(econ_hh ~ contact + as.factor(female) + age + education + income + as.factor(east), data)
mod4 <- lm(econ_nat ~ contact + as.factor(female) + age + education + income + as.factor(east), data)
mod5 <- lm(nat_id ~ contact + as.factor(female) + age + education + income + as.factor(east), data)
mod6 <- lm(ger_cult ~ contact + as.factor(female) + age + education + income + as.factor(east), data)
stargazer(mod0, mod1, mod2, mod3, mod4, mod5, mod6,
          omit.stat=c("f", "ser", "bic", "ll", "adj.rsq"), 
          star.cutoffs = c(0.05, 0.01, NA), 
          column.sep.width="1pt", font.size="small", digits=2, 
          dep.var.caption="Outcome variable:",
          dep.var.labels = c("Threat factor", "Violence Com", "Violence Nat",
                             "Econ HH", "Econ Nat",
                             "Identity", "Culture"))




#### Table OA8.2: The Effect of Contact and Left-Right Affinity (binary) on
#### Immigration-Related Threat, Germany
mod0 <- lm(threat ~ contact*right + as.factor(female) + age + education + income + as.factor(east), data)
mod1 <- lm(violence_com ~ contact*right + as.factor(female) + age + education + income + as.factor(east), data)
mod2 <- lm(violence_nat ~ contact*right + as.factor(female) + age + education + income + as.factor(east), data)
mod3 <- lm(econ_hh ~ contact*right + as.factor(female) + age + education + income + as.factor(east), data)
mod4 <- lm(econ_nat ~ contact*right + as.factor(female) + age + education + income + as.factor(east), data)
mod5 <- lm(nat_id ~ contact*right + as.factor(female) + age + education + income + as.factor(east), data)
mod6 <- lm(ger_cult ~ contact*right + as.factor(female) + age + education + income + as.factor(east), data)
stargazer(mod0, mod1, mod2, mod3, mod4, mod5, mod6,
          omit.stat=c("f", "ser", "bic", "ll", "adj.rsq"), 
          star.cutoffs = c(0.05, 0.01, NA), 
          column.sep.width="1pt", font.size="small", digits=2, 
          dep.var.caption="Outcome variable:",
          dep.var.labels = c("Threat factor", "Violence Com", "Violence Nat",
                             "Econ HH", "Econ Nat",
                             "Identity", "Culture"))




#### Figure OA8.1: Marginal Effect of Contact on Threat Perceptions, Germany
#pdf("FigOA8.1.pdf", height=8,width=7)
layout(matrix(c(0,0,0,1,1,1,1,1,0,0,0,
                2,2,2,2,2,0,3,3,3,3,3,
                4,4,4,4,4,0,5,5,5,5,5,
                6,6,6,6,6,0,7,7,7,7,7), 4, 11, byrow = TRUE))
interaction_plot_binary(mod0, "contact", "right", "contact:right",
                        xlabel="Marginal Effect of Contact",
                        ylabel="",
                        ymax=0.5, ymin=-0.6,
                        factor_labels=c("Leftist", "Rightist"),
                        title="Threat (score)")
interaction_plot_binary(mod1, "contact", "right", "contact:right",
                        xlabel="Marginal Effect of Contact",
                        ylabel="",
                        ymax=0.25, ymin=-0.35,
                        factor_labels=c("Leftist", "Rightist"),
                        title="Violence, community")
interaction_plot_binary(mod2, "contact", "right", "contact:right",
                        xlabel="Marginal Effect of Contact",
                        ylabel="",
                        ymax=0.25, ymin=-0.35,
                        factor_labels=c("Leftist", "Rightist"),
                        title="Violence, national")
interaction_plot_binary(mod3, "contact", "right", "contact:right",
                        xlabel="Marginal Effect of Contact",
                        ylabel="",
                        ymax=0.25, ymin=-0.35,
                        factor_labels=c("Leftist", "Rightist"),
                        title="Economy, household")
interaction_plot_binary(mod4, "contact", "right", "contact:right",
                        xlabel="Marginal Effect of Contact",
                        ylabel="",
                        ymax=0.25, ymin=-0.35,
                        factor_labels=c("Leftist", "Rightist"),
                        title="Economy, national")
interaction_plot_binary(mod5, "contact", "right", "contact:right",
                        xlabel="Marginal Effect of Contact",
                        ylabel="",
                        ymax=0.25, ymin=-0.35,
                        factor_labels=c("Leftist", "Rightist"),
                        title="National identity")
interaction_plot_binary(mod6, "contact", "right", "contact:right",
                        xlabel="Marginal Effect of Contact",
                        ylabel="",
                        ymax=0.25, ymin=-0.35,
                        factor_labels=c("Leftist", "Rightist"),
                        title="German culture")
par(mfrow=c(1,1))
#dev.off()




#### Table OA8.3: The Effect of Contact and Left-Right Affinity (left-right-nonvoter)
## on Immigration-Related Threat, Germany
mod0 <- lm(threat ~ contact*voters + as.factor(female) + age + education + income + as.factor(east), data)
mod1 <- lm(violence_com ~ contact*voters + as.factor(female) + age + education + income + as.factor(east), data)
mod2 <- lm(violence_nat ~ contact*voters + as.factor(female) + age + education + income + as.factor(east), data)
mod3 <- lm(econ_hh ~ contact*voters + as.factor(female) + age + education + income + as.factor(east), data)
mod4 <- lm(econ_nat ~ contact*voters + as.factor(female) + age + education + income + as.factor(east), data)
mod5 <- lm(nat_id ~ contact*voters + as.factor(female) + age + education + income + as.factor(east), data)
mod6 <- lm(ger_cult ~ contact*voters + as.factor(female) + age + education + income + as.factor(east), data)
stargazer(mod0, mod1, mod2, mod3, mod4, mod5, mod6,
          omit.stat=c("f", "ser", "bic", "ll", "adj.rsq"), 
          star.cutoffs = c(0.05, 0.01, NA), 
          column.sep.width="1pt", font.size="small", digits=2, 
          dep.var.caption="Outcome variable:",
          dep.var.labels = c("Threat factor", "Violence Com", "Violence Nat",
                             "Econ HH", "Econ Nat",
                             "Identity", "Culture"))




#### Figure OA8.2: Marginal Effect of Contact on Threat Perceptions, Germany
#pdf("FigOA8.2.pdf", height=8,width=7)
layout(matrix(c(0,0,0,1,1,1,1,1,0,0,0,
                2,2,2,2,2,0,3,3,3,3,3,
                4,4,4,4,4,0,5,5,5,5,5,
                6,6,6,6,6,0,7,7,7,7,7), 4, 11, byrow = TRUE))
par(mar = c(5,4.4,4,2) + 0.1)
interaction_plot_binary3(mod0, "contact", "voters1", "contact:voters1",
                         "voters2", "contact:voters2",
                         xlabel="Marginal Effect of Contact",
                         ylabel="", ymax=0.5, ymin=-0.6,
                         title="Threat (score)")
interaction_plot_binary3(mod1, "contact", "voters1", "contact:voters1",
                         "voters2", "contact:voters2",
                         xlabel="Marginal Effect of Contact",
                         ylabel="", ymax=0.25, ymin=-0.35,
                         title="Violence, community")
interaction_plot_binary3(mod2, "contact", "voters1", "contact:voters1",
                         "voters2", "contact:voters2",
                         xlabel="Marginal Effect of Contact",
                         ylabel="", ymax=0.25, ymin=-0.35,
                         title="Violence, national")
interaction_plot_binary3(mod3, "contact", "voters1", "contact:voters1",
                         "voters2", "contact:voters2",
                         xlabel="Marginal Effect of Contact",
                         ylabel="", ymax=0.25, ymin=-0.35,
                         title="Economy, household")
interaction_plot_binary3(mod4, "contact", "voters1", "contact:voters1",
                         "voters2", "contact:voters2",
                         xlabel="Marginal Effect of Contact",
                         ylabel="", ymax=0.25, ymin=-0.35,
                         title="Economy, national")
interaction_plot_binary3(mod5, "contact", "voters1", "contact:voters1",
                         "voters2", "contact:voters2",
                         xlabel="Marginal Effect of Contact",
                         ylabel="", ymax=0.25, ymin=-0.35,
                         title="National identity")
interaction_plot_binary3(mod6, "contact", "voters1", "contact:voters1",
                         "voters2", "contact:voters2",
                         xlabel="Marginal Effect of Contact",
                         ylabel="", ymax=0.25, ymin=-0.35,
                         title="German culture")
par(mfrow=c(1,1))
par(mar = c(5,4,4,2) + 0.1)
#dev.off()




#### Table OA8.4: The Effect of Contact and Left-Right Affinity (continuous) on
#### Immigration-Related Threat, Germany
mod0 <- lm(threat ~ contact*lrscale + as.factor(female) + age + education + income + as.factor(east), data)
mod1 <- lm(violence_com ~ contact*lrscale + as.factor(female) + age + education + income + as.factor(east), data)
mod2 <- lm(violence_nat ~ contact*lrscale + as.factor(female) + age + education + income + as.factor(east), data)
mod3 <- lm(econ_hh ~ contact*lrscale + as.factor(female) + age + education + income + as.factor(east), data)
mod4 <- lm(econ_nat ~ contact*lrscale + as.factor(female) + age + education + income + as.factor(east), data)
mod5 <- lm(nat_id ~ contact*lrscale + as.factor(female) + age + education + income + as.factor(east), data)
mod6 <- lm(ger_cult ~ contact*lrscale + as.factor(female) + age + education + income + as.factor(east), data)
stargazer(mod0, mod1, mod2, mod3, mod4, mod5, mod6,
          omit.stat=c("f", "ser", "bic", "ll", "adj.rsq"), 
          star.cutoffs = c(0.05, 0.01, NA), 
          column.sep.width="1pt", font.size="small", digits=2, 
          dep.var.caption="Outcome variable:",
          dep.var.labels = c("Threat factor", "Violence Com", "Violence Nat",
                             "Econ HH", "Econ Nat",
                             "Identity", "Culture"))




#### Figure OA8.3: Marginal Effect of Contact on Threat Perceptions, Germany
#pdf("FigOA8.3.pdf", height=8,width=7)
layout(matrix(c(0,0,0,1,1,1,1,1,0,0,0,
                2,2,2,2,2,0,3,3,3,3,3,
                4,4,4,4,4,0,5,5,5,5,5,
                6,6,6,6,6,0,7,7,7,7,7), 4, 11, byrow = TRUE))
interaction_plot_continuous(mod0, "contact", "lrscale", "contact:lrscale",
                            rugplot=F, histogram=T,
                            ylabel="Marginal Effect of Contact",
                            xlabel="Continuous left-right score",
                            ymax=0.65, ymin=-0.85,
                            title="Threat (score)")
interaction_plot_continuous(mod1, "contact", "lrscale", "contact:lrscale",
                            rugplot=F, histogram=T,
                            ylabel="Marginal Effect of Contact",
                            xlabel="Continuous left-right score",
                            ymax=0.35, ymin=-0.45,
                            title="Violence, community")
interaction_plot_continuous(mod2, "contact", "lrscale", "contact:lrscale",
                            rugplot=F, histogram=T,
                            ylabel="Marginal Effect of Contact",
                            xlabel="Continuous left-right score",
                            ymax=0.35, ymin=-0.45,
                            title="Violence, national")
interaction_plot_continuous(mod3, "contact", "lrscale", "contact:lrscale",
                            rugplot=F, histogram=T,
                            ylabel="Marginal Effect of Contact",
                            xlabel="Continuous left-right score",
                            ymax=0.35, ymin=-0.45,
                            title="Economy, household")
interaction_plot_continuous(mod4, "contact", "lrscale", "contact:lrscale",
                            rugplot=F, histogram=T,
                            ylabel="Marginal Effect of Contact",
                            xlabel="Continuous left-right score",
                            ymax=0.35, ymin=-0.45,
                            title="Economy, national")
interaction_plot_continuous(mod5, "contact", "lrscale", "contact:lrscale",
                            rugplot=F, histogram=T,
                            ylabel="Marginal Effect of Contact",
                            xlabel="Continuous left-right score",
                            ymax=0.35, ymin=-0.45,
                            title="National identity")
interaction_plot_continuous(mod6, "contact", "lrscale", "contact:lrscale",
                            rugplot=F, histogram=T,
                            ylabel="Marginal Effect of Contact",
                            xlabel="Continuous left-right score",
                            ymax=0.35, ymin=-0.45,
                            title="German culture")
par(mfrow=c(1,1))
#dev.off()




#### Table OA8.5: The Effect of Contact and Anti-Immigration Rhetoric on
#### Immigration-Related Threat, Germany
mod0 <- lm(threat ~ contact*chimmig + as.factor(female) + age + education + income + as.factor(east), data)
mod1 <- lm(violence_com ~ contact*chimmig + as.factor(female) + age + education + income + as.factor(east), data)
mod2 <- lm(violence_nat ~ contact*chimmig + as.factor(female) + age + education + income + as.factor(east), data)
mod3 <- lm(econ_hh ~ contact*chimmig + as.factor(female) + age + education + income + as.factor(east), data)
mod4 <- lm(econ_nat ~ contact*chimmig + as.factor(female) + age + education + income + as.factor(east), data)
mod5 <- lm(nat_id ~ contact*chimmig + as.factor(female) + age + education + income + as.factor(east), data)
mod6 <- lm(ger_cult ~ contact*chimmig + as.factor(female) + age + education + income + as.factor(east), data)
stargazer(mod0, mod1, mod2, mod3, mod4, mod5, mod6,
          omit.stat=c("f", "ser", "bic", "ll", "adj.rsq"), 
          star.cutoffs = c(0.05, 0.01, NA), 
          column.sep.width="1pt", font.size="small", digits=2, 
          dep.var.caption="Outcome variable:",
          dep.var.labels = c("Threat factor", "Violence Com", "Violence Nat",
                             "Econ HH", "Econ Nat",
                             "Identity", "Culture"))




#### Figure OA8.4: Marginal Effect of Contact on Threat Perceptions, Germany
#pdf("FigOA8.4.pdf", height=8,width=7)
layout(matrix(c(0,0,0,1,1,1,1,1,0,0,0,
                2,2,2,2,2,0,3,3,3,3,3,
                4,4,4,4,4,0,5,5,5,5,5,
                6,6,6,6,6,0,7,7,7,7,7), 4, 11, byrow = TRUE))
interaction_plot_continuous(mod0, "contact", "chimmig", "contact:chimmig",
                            rugplot=F, histogram=T,
                            ylabel="Marginal Effect of Contact",
                            xlabel="CHES Immigration score",
                            ymax=0.80, ymin=-0.60,
                            title="Threat (score)")
interaction_plot_continuous(mod1, "contact", "chimmig", "contact:chimmig",
                            rugplot=F, histogram=T,
                            ylabel="Marginal Effect of Contact",
                            xlabel="CHES Immigration score",
                            ymax=0.45, ymin=-0.35,
                            title="Violence, community")
interaction_plot_continuous(mod2, "contact", "chimmig", "contact:chimmig",
                            rugplot=F, histogram=T,
                            ylabel="Marginal Effect of Contact",
                            xlabel="CHES Immigration score",
                            ymax=0.45, ymin=-0.35,
                            title="Violence, national")
interaction_plot_continuous(mod3, "contact", "chimmig", "contact:chimmig",
                            rugplot=F, histogram=T,
                            ylabel="Marginal Effect of Contact",
                            xlabel="CHES Immigration score",
                            ymax=0.45, ymin=-0.35,
                            title="Economy, household")
interaction_plot_continuous(mod4, "contact", "chimmig", "contact:chimmig",
                            rugplot=F, histogram=T,
                            ylabel="Marginal Effect of Contact",
                            xlabel="CHES Immigration score",
                            ymax=0.45, ymin=-0.35,
                            title="Economy, national")
interaction_plot_continuous(mod5, "contact", "chimmig", "contact:chimmig",
                            rugplot=F, histogram=T,
                            ylabel="Marginal Effect of Contact",
                            xlabel="CHES Immigration score",
                            ymax=0.45, ymin=-0.35,
                            title="National identity")
interaction_plot_continuous(mod6, "contact", "chimmig", "contact:chimmig",
                            rugplot=F, histogram=T,
                            ylabel="Marginal Effect of Contact",
                            xlabel="CHES Immigration score",
                            ymax=0.45, ymin=-0.35,
                            title="German culture")
par(mfrow=c(1,1))
#dev.off()




#### Table OA8.6: The Effect of Contact and Anti-Immigration Rhetoric on
#### Immigration-Related Threat Recoded, Germany
## chimmig2 -- recode CDU to SPD value
## 1 - CDU 5.727273 --> 3.909091
## 2 - SPD 3.909091
data$chimmig2<- data$chimmig
data$chimmig2[data$vote2013==1] <- 3.909091
## chimmig3 -- include CSU voters separately
## 1 - CDU 5.727273
## 1 - CSU 7.454545
data$chimmig3<- data$chimmig
data$chimmig3[(data$vote2013==1) & (data$state==9)] <- 7.454545

mod1 <- lm(threat ~ contact*chimmig + as.factor(female) + age + education + income + as.factor(east), data)
mod2 <- lm(threat ~ contact*chimmig2 + as.factor(female) + age + education + income + as.factor(east), data)
mod3 <- lm(threat ~ contact*chimmig3 + as.factor(female) + age + education + income + as.factor(east), data)
stargazer(mod1, mod2, mod3,
          omit.stat=c("f", "ser", "bic", "ll", "adj.rsq"), 
          star.cutoffs = c(0.05, 0.01, NA), 
          column.sep.width="1pt", font.size="small", digits=2, 
          dep.var.caption="Outcome variable:",
          dep.var.labels = "Threat Factor Score")




#### Table OA8.7: The Effect of 3 Contact Measures on Immigration-Related Threat,
#### Germany
data2 <- subset(data, !is.na(right))
mod1 <- lm(threat ~ contact1 + as.factor(female) + age + education + income + as.factor(east), data2)
mod2 <- lm(threat ~ contact1*right + as.factor(female) + age + education + income + as.factor(east), data)
mod3 <- lm(threat ~ contact1*lrscale + as.factor(female) + age + education + income + as.factor(east), data)
mod4 <- lm(threat ~ contact2 + as.factor(female) + age + education + income + as.factor(east), data2)
mod5 <- lm(threat ~ contact2*right + as.factor(female) + age + education + income + as.factor(east), data)
mod6 <- lm(threat ~ contact2*lrscale + as.factor(female) + age + education + income + as.factor(east), data)
mod7 <- lm(threat ~ contact3 + as.factor(female) + age + education + income + as.factor(east), data2)
mod8 <- lm(threat ~ contact3*right + as.factor(female) + age + education + income + as.factor(east), data)
mod9 <- lm(threat ~ contact3*lrscale + as.factor(female) + age + education + income + as.factor(east), data)
stargazer(mod1, mod2, mod3, mod4, mod5, mod6, mod7, mod8, mod9,
          omit.stat=c("f", "ser", "bic", "ll", "adj.rsq"), 
          star.cutoffs = c(0.1, 0.05, 0.01), 
          column.sep.width="1pt", font.size="small", digits=2, 
          dep.var.caption="Outcome variable:",
          dep.var.labels = "Threat Factor Score")

